A Jupyter notebook that analyzes trending YouTube video data to reveal patterns such as popular categories, view counts, and trending dynamics across regions. The notebook demonstrates data cleaning, aggregation, and visualization techniques.

Notebook: youtube_trending_videos_analysis.ipynb

• Loads the dataset (CSV) and performs exploratory data analysis (EDA) using pandas.
• Computes summary statistics (top channels, most-viewed videos, common categories) and visualizes trends using matplotlib and seaborn.
• Includes plots such as time-series, histograms, bar charts, and correlation visuals to uncover insights.

• Python 3.x
• pandas, numpy, matplotlib, seaborn (install with pip install pandas numpy matplotlib seaborn)

1. Install dependencies: pip install pandas numpy matplotlib seaborn
2. Launch Jupyter and open the notebook: jupyter notebook youtube_trending_videos_analysis.ipynb
3. Run cells in order to reproduce the analysis and plots.

This notebook is an instructive example of working with real-world CSV datasets for EDA and visualization. It is easy to extend by adding predictive modeling, per-country comparisons, or interactive dashboards.